



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a      :  Optional[Any]			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		str  ):





			"""simple docstring"""


			UpperCAmelCase_:		Tuple							      =     SwinConfig.from_pretrained(
			    """microsoft/swin-tiny-patch4-window7-224"""		,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  )
			UpperCAmelCase_:		Optional[Any]							      =     MaskFormerConfig(backbone_config=lowerCAmelCase__  )

			UpperCAmelCase_:		Union[str, Any]							      =     """huggingface/label-files"""
			if "ade20k-full" in model_name:
						# this should be ok
						UpperCAmelCase_:		Any							      =     8_4_7
						UpperCAmelCase_:		List[Any]							      =     """maskformer-ade20k-full-id2label.json"""
			elif "ade" in model_name:
						# this should be ok
						UpperCAmelCase_:		str							      =     1_5_0
						UpperCAmelCase_:		List[Any]							      =     """ade20k-id2label.json"""
			elif "coco-stuff" in model_name:
						# this should be ok
						UpperCAmelCase_:		str							      =     1_7_1
						UpperCAmelCase_:		str							      =     """maskformer-coco-stuff-id2label.json"""
			elif "coco" in model_name:
						# TODO
						UpperCAmelCase_:		List[str]							      =     1_3_3
						UpperCAmelCase_:		Any							      =     """coco-panoptic-id2label.json"""
			elif "cityscapes" in model_name:
						# this should be ok
						UpperCAmelCase_:		Optional[int]							      =     1_9
						UpperCAmelCase_:		Dict							      =     """cityscapes-id2label.json"""
			elif "vistas" in model_name:
						# this should be ok
						UpperCAmelCase_:		Dict							      =     6_5
						UpperCAmelCase_:		Any							      =     """mapillary-vistas-id2label.json"""

			UpperCAmelCase_:		int							      =     json.load(open(hf_hub_download(lowerCAmelCase__		,						lowerCAmelCase__		,						repo_type="""dataset"""  )		,						"""r"""  )  )
			UpperCAmelCase_:		Optional[int]							      =     {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}

			return config







def  lowerCAmelCase_	(lowerCAmelCase__:		Any  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     []
			# stem
			# fmt: off
			rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")  )
			rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")  )
			rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")  )
			rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")  )
			# stages
			for i in range(len(config.backbone_config.depths  )  ):
						for j in range(config.backbone_config.depths[i]  ):
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')  )

						if i < 3:
									rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')  )
									rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')  )
									rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')  )
						rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')  )
						rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')  )

			# FPN
			rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")  )
			rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")  )
			rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")  )
			for source_index, target_index in zip(range(3		,						0		,						-1  )		,						range(0		,						3  )  ):
						rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')  )
						rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')  )
						rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')  )
						rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')  )
						rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')  )
						rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')  )
			rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")  )
			rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")  )

			# Transformer decoder
			for idx in range(config.decoder_config.decoder_layers  ):
						# self-attention out projection
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')  )
						# cross-attention out projection
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')  )
						# MLP 1
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias')  )
						# MLP 2
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias')  )
						# layernorm 1 (self-attention layernorm)
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')  )
						# layernorm 2 (cross-attention layernorm)
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')  )
						# layernorm 3 (final layernorm)
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')  )

			rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")  )
			rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")  )

			# heads on top
			rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")  )

			rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")  )
			rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")  )

			rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")  )
			rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")  )

			for i in range(3  ):
						rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias')  )
			# fmt: on

			return rename_keys







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		str		,						lowerCAmelCase__:		Union[str, Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[Any]							      =     dct.pop(lowerCAmelCase__  )
			UpperCAmelCase_:		Dict							      =     val







def  lowerCAmelCase_	(lowerCAmelCase__:		str		,						lowerCAmelCase__:		List[str]  ):





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]							      =     [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
			for i in range(len(backbone_config.depths  )  ):
						UpperCAmelCase_:		Union[str, Any]							      =     num_features[i]
						for j in range(backbone_config.depths[i]  ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									UpperCAmelCase_:		int							      =     state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'  )
									UpperCAmelCase_:		Any							      =     state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'  )
									# next, add query, keys and values (in that order) to the state dict
									UpperCAmelCase_:		Any							      =     in_proj_weight[:dim, :]
									UpperCAmelCase_:		List[Any]							      =     in_proj_bias[: dim]
									UpperCAmelCase_:		Any							      =     in_proj_weight[
									    dim : dim * 2, :
									]
									UpperCAmelCase_:		Optional[int]							      =     in_proj_bias[
									    dim : dim * 2
									]
									UpperCAmelCase_:		Dict							      =     in_proj_weight[
									    -dim :, :
									]
									UpperCAmelCase_:		int							      =     in_proj_bias[-dim :]
									# fmt: on







def  lowerCAmelCase_	(lowerCAmelCase__:		int		,						lowerCAmelCase__:		Optional[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     config.decoder_config.hidden_size
			for idx in range(config.decoder_config.decoder_layers  ):
						# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
						UpperCAmelCase_:		Dict							      =     state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'  )
						UpperCAmelCase_:		List[Any]							      =     state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'  )
						# next, add query, keys and values (in that order) to the state dict
						UpperCAmelCase_:		Any							      =     in_proj_weight[: hidden_size, :]
						UpperCAmelCase_:		Any							      =     in_proj_bias[:config.hidden_size]
						UpperCAmelCase_:		Dict							      =     in_proj_weight[hidden_size : hidden_size * 2, :]
						UpperCAmelCase_:		Dict							      =     in_proj_bias[hidden_size : hidden_size * 2]
						UpperCAmelCase_:		int							      =     in_proj_weight[-hidden_size :, :]
						UpperCAmelCase_:		Any							      =     in_proj_bias[-hidden_size :]
						# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
						UpperCAmelCase_:		int							      =     state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'  )
						UpperCAmelCase_:		Any							      =     state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'  )
						# next, add query, keys and values (in that order) to the state dict
						UpperCAmelCase_:		str							      =     in_proj_weight[: hidden_size, :]
						UpperCAmelCase_:		Dict							      =     in_proj_bias[:config.hidden_size]
						UpperCAmelCase_:		Any							      =     in_proj_weight[hidden_size : hidden_size * 2, :]
						UpperCAmelCase_:		Any							      =     in_proj_bias[hidden_size : hidden_size * 2]
						UpperCAmelCase_:		Dict							      =     in_proj_weight[-hidden_size :, :]
						UpperCAmelCase_:		Dict							      =     in_proj_bias[-hidden_size :]
			# fmt: on







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase_:		Any							      =     Image.open(requests.get(lowerCAmelCase__		,						stream=lowerCAmelCase__  ).raw  )
			return im







@torch.no_grad()
def  lowerCAmelCase_	(lowerCAmelCase__:		str		,						lowerCAmelCase__:		str		,						lowerCAmelCase__:		str		,						lowerCAmelCase__:		bool = False  ):





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     get_maskformer_config(lowerCAmelCase__  )

			# load original state_dict
			with open(lowerCAmelCase__		,						"""rb"""  ) as f:
						UpperCAmelCase_:		List[Any]							      =     pickle.load(lowerCAmelCase__  )
			UpperCAmelCase_:		Optional[int]							      =     data["""model"""]

			# for name, param in state_dict.items():
			#     print(name, param.shape)

			# rename keys
			UpperCAmelCase_:		Any							      =     create_rename_keys(lowerCAmelCase__  )
			for src, dest in rename_keys:
						rename_key(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			read_in_swin_q_k_v(lowerCAmelCase__		,						config.backbone_config  )
			read_in_decoder_q_k_v(lowerCAmelCase__		,						lowerCAmelCase__  )

			# update to torch tensors
			for key, value in state_dict.items():
						UpperCAmelCase_:		Dict							      =     torch.from_numpy(lowerCAmelCase__  )

			# load 🤗 model
			UpperCAmelCase_:		List[str]							      =     MaskFormerForInstanceSegmentation(lowerCAmelCase__  )
			model.eval()

			for name, param in model.named_parameters():
						print(lowerCAmelCase__		,						param.shape  )

			UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     model.load_state_dict(lowerCAmelCase__		,						strict=lowerCAmelCase__  )
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(lowerCAmelCase__  ) == 0, F'Unexpected keys: {unexpected_keys}'

			# verify results
			UpperCAmelCase_:		int							      =     prepare_img()
			if "vistas" in model_name:
						UpperCAmelCase_:		Optional[Any]							      =     6_5
			elif "cityscapes" in model_name:
						UpperCAmelCase_:		Dict							      =     6_5_5_3_5
			else:
						UpperCAmelCase_:		str							      =     2_5_5
			UpperCAmelCase_:		Optional[int]							      =     True if """ade""" in model_name else False
			UpperCAmelCase_:		int							      =     MaskFormerImageProcessor(ignore_index=lowerCAmelCase__		,						reduce_labels=lowerCAmelCase__  )

			UpperCAmelCase_:		Dict							      =     image_processor(lowerCAmelCase__		,						return_tensors="""pt"""  )

			UpperCAmelCase_:		Union[str, Any]							      =     model(**lowerCAmelCase__  )

			print("""Logits:"""		,						outputs.class_queries_logits[0, :3, :3]  )

			if model_name == "maskformer-swin-tiny-ade":
						UpperCAmelCase_:		Dict							      =     torch.tensor(
						    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]  )
			assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,						lowerCAmelCase__		,						atol=1e-4  )
			print("""Looks ok!"""  )

			if pytorch_dump_folder_path is not None:
						print(F'Saving model and image processor to {pytorch_dump_folder_path}'  )
						Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
						model.save_pretrained(lowerCAmelCase__  )
						image_processor.save_pretrained(lowerCAmelCase__  )

			if push_to_hub:
						print("""Pushing model and image processor to the hub..."""  )
						model.push_to_hub(F'nielsr/{model_name}'  )
						image_processor.push_to_hub(F'nielsr/{model_name}'  )


if __name__ == "__main__":
	a      :  str			     =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='maskformer-swin-tiny-ade',
	    type=str,
	    help=('Name of the MaskFormer model you\'d like to convert',),
	)
	parser.add_argument(
	    '--checkpoint_path',
	    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
	    type=str,
	    help='Path to the original state dict (.pth file).',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	a      :  int			     =			parser.parse_args()
	convert_maskformer_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)











import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


a      :  Union[str, Any]			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	CLIPConfig

			A      		=	['''CLIPEncoderLayer''']

			def __init__(self,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						super().__init__(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     CLIPVisionModelWithProjection(config.vision_config					)

						UpperCAmelCase_:		Tuple							      =     nn.Linear(config.vision_config.projection_dim,   1					)
						UpperCAmelCase_:		Tuple							      =     nn.Linear(config.vision_config.projection_dim,   1					)

			@torch.no_grad()
			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=0.5,   SCREAMING_SNAKE_CASE_=0.5					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     self.vision_model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		List[str]							      =     self.p_head(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     nsfw_detected.flatten()
						UpperCAmelCase_:		List[Any]							      =     nsfw_detected > p_threshold
						UpperCAmelCase_:		Any							      =     nsfw_detected.tolist()

						if any(SCREAMING_SNAKE_CASE_					):
									logger.warning(
									    """Potential NSFW content was detected in one or more images. A black image will be returned instead."""
									    """ Try again with a different prompt and/or seed."""					)

						for idx, nsfw_detected_ in enumerate(SCREAMING_SNAKE_CASE_					):
									if nsfw_detected_:
												UpperCAmelCase_:		Tuple							      =     np.zeros(images[idx].shape					)

						UpperCAmelCase_:		str							      =     self.w_head(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     watermark_detected.flatten()
						UpperCAmelCase_:		Tuple							      =     watermark_detected > w_threshold
						UpperCAmelCase_:		int							      =     watermark_detected.tolist()

						if any(SCREAMING_SNAKE_CASE_					):
									logger.warning(
									    """Potential watermarked content was detected in one or more images. A black image will be returned instead."""
									    """ Try again with a different prompt and/or seed."""					)

						for idx, watermark_detected_ in enumerate(SCREAMING_SNAKE_CASE_					):
									if watermark_detected_:
												UpperCAmelCase_:		Any							      =     np.zeros(images[idx].shape					)

						return images, nsfw_detected, watermark_detected






